Review on engine vibration fault analysis based on data mining

Abstract

Through equipment monitoring, the uptimes of machines are enhanced in the industrial applications. The unpredicted failures risks are minimized by the proper equipment monitoring. The machine vibrations are increased caused by the failure modes. The vibration data requires effective analysis by the accurate assessment of the machine equipment. For fault feature selection and detection of faults in rotating equipment, the empirical knowledge is required. Low efficiency of the methods and motor speed control are the main drawbacks of the existing techniques. So the basic aim of this paper is the detection of rotating equipment faults by utilizing the vibration analysis. The motor vibration is analyzed and monitored using spectrum analysis. The spectral content are extracted and fed into the classifier like k-Nearest neighbors (KNN), back-propagation neural network BPNN, Sparse Representation Classifier (SRC), Support vector machine (SVM) and Random Forest (RF) for the type of failure prediction and analyze the unbalance condition (UNB), bearing faults (BDF), and broken rotor bars (BRB) faults. The RF classifier is better as compared to other classifiers in terms of accuracy, precision and recalls values by approximately 10.92 %, 11.03 % and 20.13 % respectively.